Field
Apparatuses and methods consistent with the exemplary embodiments relate to a system, apparatus, method for processing a natural language, and a non-transitory computer readable recording medium, and more particularly, to a natural language processing system, apparatus, and method that determine an appropriate system behavior for a user utterance of a compound sentence form in the natural language processing system such as a spoken dialog system, a question answering system, or a chatting system and selectively process the system behavior according to whether a plurality of operations intended by a user are operations that may be sequentially processed in a system, e.g., in an image display apparatus, and a non-transitory computer readable recording medium.
Description of the Related Art
In general, a machine translation means that a computer system automatically converts a natural language sentence F of an input language into a natural language sentence E of a target language. A statistical machine translation of the machine translation learns a machine translation model based on training data and performs a machine translation based on the learned model. In more detail, the statistical machine translation is a process of finding E that makes probability Pr(E|F) of E maximum when F is given. In other words, E is a best translation result of F. This may be expressed as in Equation 1 below.E*=argmaxEPr(E|F)  (1)
Equation 2 may be acquired below by applying Bayes' rule to Equation 1 above to decompose Pr(E|F).E*=argmaxEPr(E)Pr(F|E)  (2)
Here, Pr(F|E) denotes a probability that a translation model will be translated into F when the translation model is given, i.e., indicates how appropriate the translation of E into F is. The translation model is learned based on training data about a bilingual language.
Pr(E) refers to a probability that E will appear as a language model in a corresponding language and indicates how natural E is. The language model is learned based on training data about a monolingual language.
An existing natural language processing system analyzes an input sentence into morpheme information, a syntax structure, semantics, etc. Here, one input sentence is a basic sentence having a minimum size or a sentence including a plurality of basic sentences, i.e., a complex sentence.
The basic sentences forming the complex sentence are connected to one another in various forms.
For example, there may be a natural language processing system that recognizes and performs a voice command associated with a TV program.
A TV user may utter a complex sentence “Record OCN news and show me Family Guy” by using a natural language processing system. In this complex sentence, basic sentences “Record OCN news” and “Show me Family Guy” are connected to each other through conjunction “and”.
Also, in a few of languages such as Korean, when sentences are connected to one another by conjunctions, the sentences may be modified. The TV user may utter a complex sentence “Record Muhan Challenge and play 1 Night 2 Days” in the natural language processing system. In this complex sentence, basic sentences “Record Muhan Challenge” and “Play 1 Night and 2 Days” are connected to each other by conjunction “and”.
The TV user may utter a complex sentence “Record OCN news and show me Family Guy” into the natural language processing system. This complex sentence is produced if the TV user consecutively utters two sentences without a conjunction.
However, the existing natural language processing system is difficult to process a complex sentence as described above, and thus a performance of the existing natural language processing system is degraded.